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Let Your Data Tell a Story: Keys to Predictive Analytics-Led Decision-Making

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By Tom Haushalter

You envision a reality in which your institution’s key stakeholders—top administrators, provosts, deans, faculty, institutional research analysts, IT, and so on—are all connected to and freely share the same data sets and dashboards, aligning everyone to the tangible benefits of predictive analytics and data-informed decision-making.

That scenario is probably not your experience. After all, less than 50% of higher-ed institutions prioritize predictive analytics in their strategies to boost enrollment, stave off attrition, or improve the overall student experience.

Changing the institutional culture from one of “seeing what sticks” to one that leverages these data-informed insights into action and for measurable impact may seem like an insurmountable hurdle.

But in many cases, the ideal, collaborative, data-driven culture that harnesses the power of predictive analytics was built from sparse resources—which is to say, it can be done affordably and without a lot of training.

Predictive Modeling Takes the Right Kind of Tool

And it all begins with data prep. With Construct, seamlessly connect to any data format, run processes to blend, cleanse, and aggregate that data for analysis and reporting. An easy-to-learn, drag-and-drop workflow enables any level of user to build step-by-step analyses—with no coding required.

Then, using Predict, discover the wonders of predictive modeling. Quickly measure your data quality and patterns among variables, and with one click, automatically mine the data to identify predictive qualities in the variables.

And then keep automating. Because you can either build your own predictive models, or let Predict works its magic, auto-generating the best-fit model based on the available data.

But because transparency and model defendability is crucial, tweak and revise any of the model’s ingredients, dig into why some variables were included and others weren’t, and iterate on those outcomes quickly and easily.

Why Data Storytelling Is Important

Gaining insights from predictive analytics all sounds good on paper. And for the research analyst whose job it is to study the data, deploy the models, and generate reports, the conclusions may be easy to make.

But those decision-makers in your institution will demand that your predictive models—and the conclusions to be drawn from them—are palatable, easily relatable, and, ultimately, actionable. Consider, too, that most other won’t have as intimate a relationship with the data as you, nor will they have time to sift through a report for key takeaways. This is why data storytelling is critical.

When it comes to data articulation (i.e. communicating your data effectively), here are three things to consider:

  • Know your audience! – Determine whether stakeholders are more receptive to bite-sized, mobile-optimized takeaways, a simple one-page report, or an in-depth study that places a wide lens on industry trends. Your report may include a combination of formats, depending on the range of data-consuming appetites.
  • Find a writer! – Reporting on analytical outcomes is an opportunity to tell a story, to walk your audience through the journey that the data underpins. As with any good story, establish context, state the problem, explore probabilities, and arrive at a solution.
  • Find a designer! – Tools like Tableau and Bridge can be useful for creating data visualizations—charts and graphs that illustrate analytical outcomes—but your audience may expect it all to be packaged neatly in a PowerPoint presentation. If so, lean on a design-savvy colleague (along with a writer!) to help produce a cohesive, engaging end product.

Preparing your institution for predictive analytics-led decision-making doesn’t need to cost you dearly—in time or money. Start by investing in a right-sized data analytics solution with end-to-end predictive modeling capabilities. But don’t stop there—make data storytelling a priority. Keep stakeholders engaged and invested by making the reported data digestible, actionable, and always current.

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